ORIGINAL ARTICLE

Neural network model and isotherm study for removal of phenolfrom aqueous solution by orange peel ash

Naba Kumar Mondal • Ria Bhaumik •

Biswajit Das • Palas Roy • Jayanta Kumar Datta •

Siddhartha Bhattacharyya • Siddhartha Bhattacharjee

Received: 12 January 2014 / Accepted: 1 April 2014 / Published online: 24 April 2014

� The Author(s) 2014. This article is published with open access at Springerlink.com

Abstract Artificial Neural Network model and isotherm

study were done to predict the removal efficiency of phe-

nol. An inexpensive adsorbent was developed from orange

peel ash (OPA) for effective uptake of phenol from aque-

ous solution. The influence of different experimental

parameters (initial concentration, pH, adsorbents dose,

contact time, stirring rate and temperature) on phenol

uptake efficiency was evaluated. Phenol was adsorbed by

the OPA up to maximum of 97.34 %. Adsorption of phenol

on OPA correlated well with the Langmuir isotherm model,

implying monolayer coverage of phenol onto the surface of

the adsorbent. The maximum adsorption capacity was

found to be 3.55 mg g-1 at 303 K. Pseudo-second-order

kinetic model provided a better correlation for the experi-

mental data. Moreover, the activation energy of the

adsorption process (Ea) was found to be -18.001 kJ mol-1

indicating physorption nature of phenol onto OPA. A

negative enthalpy (DH�) value indicated that the adsorption

process was exothermic. Again multi-layer Neural Net-

work model was in very good agreement with the experi-

mental results.

Keywords Phenol � Orange peel ash � Isotherm model �Kinetics � Physorption � ANN model

Introduction

Phenols and its derivatives such as chloro and nitro phenols

are toxic and carcinogen, usually present in industrial waste

water. Very small concentration of phenol may cause

vomiting, anorexia, liver and kidney damage, fainting and

even mental disturbance. Phenols, long term affecting

pollutants can be found in industries which produce

chlorophenols that are widely used as fungicides and

insecticides in agricultural sector. Phenolic compounds in

potable water at the low level of 5 lg/L emit an unpleasant

odor and flavor and considered poisonous to aquatic life,

plants and human. Ingestion of phenols through water in

concentration from 10 to 240 mg L-1 for a long time

causes mouth irritation, diarrhea, and excretion of dark

urine and vision problems (Hegazy et al. 2013; Asma and

Zainal 2009). The WHO (2008) recommended threshold

limit of phenol in potable water as 0.001 mg L-1 (Zeng

et al. 2009), while US Environmental Protection Agency

(EPA) recommended the permissible limit of phenols in the

wastewater\1 mg L-1 (Eahart et al. 1977). Phenols can be

removed from the aqueous solution through oxidation,

precipitation, ion exchange, biodegradation, ultrasonic

degradation, solvent extraction, ozonization and decom-

position by fenton reagent (Aksu and Yener 2001; Rengaraj

et al. 2002). Adsorption is a well-established, low cost and

powerful technique for treating domestic and industrial

effluents. Recent literature highlighted the extensive use of

activated carbon in the field of wastewater treatment (Lotfy

et al. 2012; Olafadehan et al. 2012). However, Many other

adsorbents like bottom ash, brick-kilm ash, fly ash, peat,

soil, rice husk, wood, saw dust, bagasse and carbonized

bark are extensively used for removal of organic pollutants

(Ademiluy et al. 2009; Aksu and Yener 2001; Hamza et al.

2012; Rengaraj et al. 2002).

N. K. Mondal (&) � R. Bhaumik � B. Das � P. Roy � J. K. DattaDepartment of Environmental Science, The University of

Burdwan, Burdwan, West Bengal, India

e-mail: nkmenvbu@gmail.com

S. Bhattacharyya

RCC Institute of Information Technology, Canal South Road,

Kolkata 700015, India

S. Bhattacharjee

Tata Consultancy Services, Kolkata, India

123

Appl Water Sci (2015) 5:271–282

DOI 10.1007/s13201-014-0188-4

For fine tuning and predicting the adsorption mecha-

nism, many software-based models have been used (Carsky

and Do 1999; Cuco et al. 2009). In recent years, ANN has

become a popular choice among engineers and scientists as

one of the powerful tools for predicting contamination and

concentration of different effluents and chemicals in

drinking water, wastewater and aquifers and energy content

in municipal solid waste (Ogwueleka and Ogwueleka

2010). ANN model was used by Bhattacharjya et al. (2007)

and Chelani et al. (2004) to characterize the salt water in a

coastal aquifer and comparison was made between ANN

and multivariate regression.

Orange, as a kind of biological resources, is available in

large quantities in many parts of the world (Shan et al. 2012).

As the orange residue, orange peel mainly consists of cel-

lulose, hemi-cellulose and lignin in the form of carboxyl and

hydroxyl, which results in high affinity to both inorganic and

organic moiety. Although many studies in literatures have

focused on the modification of orange waste by common

chemical modifications such as alkaline and acid treatment,

the adsorption capacity and selectivity of heavy metal ions

on orangewaste arewell-documented (Ning-chuan andXue-

yi, 2012; Shan et al. 2012; Li et al. 2007). But removal of

organic pollutant using OPA is very limited. In the present

study, a simple and economic preparation of the adsorbent

from orange peel was performed and adsorption experiments

were conducted for removal of phenol from aqueous med-

ium. The equilibrium isotherm data were fitted in Langmuir,

Freundlich and DR and Temkin equations. Adsorption

kinetics of phenol onto OPA was also studied using kinetic

models. Finally the experimental results were analyzed with

the help of neural network model.

Materials and methods

Preparation of OPA

Orange peel was collected from fruit juice shop of local

market and thoroughly washed with distilled water. Orange

peels were dried up in an oven at 80 �C for overnight, cut

into small pieces and then carbonized into muffle furnace at

540 ± 2 �C for 1 h. The orange peel ash (OPA) was

ground well into a fine powder with a mortar and pestle and

sieved through a 250 lm and stored for further use.

Reagents and experimental procedure

A stock solution of phenol was prepared by dissolving

0.5 g of phenol (E. Merck Ltd., India) in double-distilled

water in a 500 mL volumetric flask. This was treated as

stock solution of phenol with a strength of 1,000 ppm. All

the intermediate phenolic solution of lower strength was

prepared from this stock solution. About 1.0 g of powder

was taken into a 250 cm3 conical flask for batch adsorption

process. The pH of the solution was adjusted to the

required level, using either HCl (0.1 mol/L) or NaOH

(0.1 mol/L) solutions whenever necessary.

Adsorption experiments

Batch adsorption study for different experimental variables

(pH; initial concentration; adsorbent dose; stirring rate;

contact time and temperature) was carried out by agitating

2.0 g of OPA with 100 mL of synthetic phenol solution in

500 mL conical flask in a temperature-controlled magnetic

stirrer (BZMS448 REMI Equipments; Pvt. Ltd., Mumbai,

India). At the end of predetermined time interval, the

content was filtered and the supernatant was analyzed for

residual concentration of phenol spectrophotometrically

(APHA et al. 1995) using UV–visible spectrophotometer

(Systronics, Vis double beem Spectro 1203).

The amount of phenol adsorbed at equilibrium, qe(mgg-1) was determined using the following equation:

qe ¼ðCi � Cf ÞV

mð1Þ

where, V = Volume (L) of the equilibrated solution

m = Mass of used OPA (g)

Ci = Initial concentration of phenol (mg L-1)

Cf = Equilibrium concentration of phenol (mg L-1)

Artificial neural network model

ANN model is a mathematical model made up of simple

processing units, which may store experimental data and

make it available for further use. The sorption efficiency of

OPA was calculated using mathematical software (Neural

Network Toolbox Neuro Solution 5 �). Twenty-four

experimental sets were used to develop the ANN model. A

multi-layer ANN with sigmoid axon transfer function was

used for input and output layers. The data generated from

batch experiments and same was used for input and desired

matrix. The trained ANN model output results were tested

with the experimental output results of phenol adsorption

on OPA. The training of the ANN model was done with six

neurons in the hidden layer.

Multi-layer perceptron

A typical multi-layer perception is a feed forward ANN

model that maps sets onto a set of appropriate outputs. It

can learn examples that are ‘‘non-linearly separable’’ like

the XOR problem. An additional layer referred to as the

272 Appl Water Sci (2015) 5:271–282

123

‘hidden layer’ is present between the input and the output

layer to handle the non-linearity problem. The number of

nodes in the input layer is depended upon the number of

variable input parameters in the training dataset. In this

present study the same number of nodes was used in the

hidden layer as in the input layer for better training.

Back propagation algorithm

The teaching of ANN model was done by back propagation.

This algorithm of adjusting the weights of the different

network layers starting from the output layer and proceed-

ing downstream is referred to the back propagation algo-

rithm. It was basically worked on the basis of delta rule and

required a data set of the desired output for many inputs.

Moreover, it is very useful for feed forward network.

Procedure

The net inputs to the network as

Aðx;wÞ ¼Xn

i¼0

xiwji þ h ð2Þ

where xi= inputs, wji= weights of connection, h=thresh-old = 0.5 (this value was taken because the input data

comprises between 0 and 1.)

Then the output was computed by the application of the

sigmoid activation function as

Ojðx;wÞ ¼1

1þ eAjðx;wÞð3Þ

The system error was given by

Eðx;w; dÞ ¼X

j

Oj x;wð Þ�dj� �2 ð4Þ

where dj= actual output

Finally the interconnection weights are adjusted using

the backpropagation algorithm as

Dvik ¼ �gð oEovik

Þ ¼ �gðoEoxi

� oxi

ovikÞ ð5Þ

where

oE

owji

¼ 2ðOj � djÞOjð1� OjÞwji ð6Þ

and

oxi

ovik¼ xið1� xiÞvik ð7Þ

where,vik = weights of hidden layer g = momentum, the

value of l was taken to be 0.02

Mean square error (MSE)

The MSE values were calculated by the following equation

MSE ¼ 1

N

XX

i¼1

NðTi � AiÞ ð8Þ

where, N = number of data point

Ti = Network predicted value at the ith data

Ai = Experimental value at the ith data

i = an index of the data.

The multi-layer perception model was used for predic-

tion of phenol removal and backpropagation algorithm was

used to train the Neural Network (Fig. 1).

Results and discussion

Adsorbent characteristics

The OPA behaves as neutral at pH zero change. The results

are shown in Fig. 2. The physicochemical properties of

OPA are summarized in Table 1. To identify the functional

Fig. 1 Neural network

architecture

Appl Water Sci (2015) 5:271–282 273

123

groups available on the surface of the investigated adsor-

bents, the IR spectra were recorded as shown in the Fig. 3a,

b. Before adsorption of fluoride OPA showed intense bands

at 3,418.60, 2,345.42, 2,363.97, 1,420.20 cm-1 and

1,048.22 cm-1. Among these, the bands at 3,418.60 cm-1

and 1,420.20 cm-1 are attributed to the hydroxyl and

amino groups, respectively. But the peak at 3,418.60 cm-1

was shifted to the 3,443.98 cm-1 and the peak at

1,420.20 cm-1 splitted to 1,625 cm-1 and 1,437 cm-1.

This is probably due to the interaction between -OH and

-NH2 functional groups of adsorbents and phenoxide ions.

Characterization of the OPA (before and after adsorption)

was also done using SEM micrograph shown in Fig. 4a, b.

It is evident from the micrograph that OPA powder was an

assemblage of fine particles, which did not have regular

fixed shape and size. But after passing phenol solution,

adsorbent surface showed uneven surface texture along

with lot of irregular surface.

Effect of pH

The initial pH of adsorption medium is one of the most

important parameters affecting the adsorption process. The

adsorption of phenol by OPA was studied at various pH

ranging from 3.0 to 7.0 (Fig. 5). It is revealed that the

adsorption of phenol by OPA was highest at pH 5.0 and

thereafter adsorption was decreased with increasing pH of

the medium. It seems to be possible because the surface

behaves as positive when pH\ pHzpc and adsorption of

anion is favored while adsorption of cation is favorable

when pH[ pHzpc (Gholami et al. 2006). The ionic fraction

of phenolate ion (ionsu) can be calculated from the fol-

lowing equation (Banat et al. 2000):

6cions ¼ 1=1þ 10PKa � pH ð9ÞAt low pH, the surface of the OPA is usually pro-

tonated and resulted in a stronger attraction for the

negatively charged phenolate ions. This is also justified

by the pHzpc value (9.1); because maximum adsorption

occurs at pH 5 which is just below the pHzpc value.

Phenol, being weakly acidic (pKa = 10), partially ionizes

in solution and transformed to negatively charged and is

directly attracted to the protonated surface of OPA by

electro-static force. Unionized phenol molecules would

also be attracted, possibly, by physical force. Non-ion-

ized phenol molecules would also be attracted, possibly,

by physical force. At high pH, OH- ions would compete

with the phenolate ions for sorption sites. Sorption of

excess of OH- ions could create a negative charge on

the OPA surface resulting repulsion of negatively

charged phenoxide ions, and therefore, adsorption is

decreased (Uddin et al. 2007). The experimental data and

ANN-calculated outputs are compared with ANN model

and shows a good performance in the prediction of the

experimental data (Fig. 11a).

Effect of adsorbent dosage

To investigate the effect of mass of adsorbent on the

adsorption of phenol, a series of adsorption experiments

were carried out with different adsorbent dosages at an

initial phenol concentration of 50 mg L-1. Fig. 6 shows

the effect of adsorbent dosage on the adsorption of phenol.

The percentage removal of phenol increased with the

increasing absorbent dosage (0.5–4.0 g). It is attributed due

to increase in adsorbent surface area and availability of

more adsorption sites (Mehrizad et al. 2009; Uddin et al.

2007). The results also revealed that the adsorption effi-

ciency increases with the increasing dose (Rengaraj et al.

2002). The experimental output data are well-fitted with

the ANN output data.

Effect of stirring rate

The effect of stirring rate on the adsorption of phenol is

shown in Fig. 7. There is a steady increase in the

Fig. 2 Plot of DpH vs. pHi

Table 1 Physico-chemical properties of OPA

Parameters Value

pH 7.8

pHzpc 9.1

Moisture content (%) 2.16

Specific gravity 0.29

Bulk density (g cm-3) 0.26

Particle density (g cm-3) 0.35

Porosity (%) 25.92

Particle size (lm) 250

Surface area (m2g-1) 141

Ash content (%) 3.6

274 Appl Water Sci (2015) 5:271–282

123

percentage of adsorption with increase in the stirring rate

from 100 to 150 rpm. The maximum adsorption occurs at

stirring rate of 150 rpm, and thereafter, the adsorption is

almost constant. The increase in adsorption at higher stir-

ring probably due to proper contact between the phenolate

ions and the adsorbent binding sites and consequently

promoting effective transfer of sorbate ions to the sorbent

sites. ANN model prediction is found to be matched with

the experimental data (Fig. 11d).

Effect of initial phenol concentration

The effect of initial concentration on adsorption of phenol

is shown in Fig. 8. Maximum adsorption is observed for

an initial concentration of 20 mg L-1. Adsorption effi-

ciency shows a decreasing trend with an increasing

4400.0 4000 3000 2000 1500 1000 400.0

1.02

4

6

8

10

12

14

16

18

20

22.0

cm-1

%T

4223.86

3933.98

3905.90

3855.76

3840.63

3418.60

2524.74

2424.70

2413.83

2363.972345.42

1420.20

1084.10

1048.22

965.96

874.52

807.96

712.83693.60

617.11519.53474.09403.99

4400.04000 3000 2000 1500 1000 400.0

6.0

8

10

12

14

16

18

20

22

24

25.0

cm-1

%T

4357.74

4348.994328.024187.90

3906.423856.19

3841.58

3443.98

2364.102345.93

1625.79 1437.64

1053.49

875.07

714.25

603.49

526.28

506.87

492.01

473.69

(a) (b)

Fig. 3 a FTIR spectra of OPA before phenol adsorption. b FTIR spectra of OPA after phenol adsorption

Fig. 4 a SEM of OPA before phenol adsorption. b SEM of OPA before phenol adsorption

2 4 6 8

68707274767880828486889092949698

100

% of adsorption % of removal

pH

% o

f ads

orpt

ion

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

qe (m

g/g)

Fig. 5 Effect of pH on adsorption of Phenol using OPA (Initial

Phenol concentration of 20.0 mg L-1; adsorbent dose of 2.0 g/

100 mL; stirring rate 150 rpm; contact time of 60 min; temperature

25 �C)

Appl Water Sci (2015) 5:271–282 275

123

concentration of phenol from 30 to 60 mg L-1. It is also

evident that the adsorption capacity of the sorbent is

increased with the increasing phenol concentration while

the adsorption yields of phenol showed the opposite trend.

It is probably due to increase in mass transfer driving

force and therefore the rate at which phenol molecules

pass from the bulk solution to the particle surface (Caturla

et al. 1998; Imagawa et al. 2000). The experimental data

and ANN-calculated output is found to be nicely matched

(Fig. 11b).

Effect of contact time

The adsorption data for the uptake of phenol versus contact

time is presented in Fig. 9. This result indicates that up to

90–95 % of the total phenol uptake occurs in the first rapid

phase (60 min) and thereafter the adsorption rate is

decreased. The higher adsorption rate at the initial period

(first 40 min) may be due to large number of vacant sites

on the adsorbent and as a result there exist increased

concentration gradients between adsorbate in solution and

adsorbate on adsorbent surface (Uddin et al. 2007). ANN

model prediction in accordance with the experimental data

(Fig. 11e).

Effect of temperature

Batch adsorption experiments were carried out at different

temperatures ranging from 298 to 313 K (Fig. 10). It is

found that with an increase in temperature from 298 to

313 K, the adsorption capacity of phenol onto OPA is

decreased from 0.973 to 0.773 mgg-1. The decrease of

adsorption capacity at higher temperature indicates that

lower temperature favors the phenol adsorption onto OPA

and the adsorption is an exothermic process. At higher

temperature entropy of the adsorbed molecule is probably

increased and subsequently escapes from the solid adsor-

bent surface to the bulk phase of solution (Bhatti et al.

2010). The experimental data are fitted well with the ANN

model (Fig. 11f; Table 2).

0 2 478

80

82

84

86

88

90

92

% of adsorpiton q

e (mg/g)

Adsorbent dose (g)

% o

f ads

orpt

ion

0

2

4

6

8

10

qe (m

g/g)

Fig. 6 Effect of adsorbent dose on adsorption of phenol using OPA

(Initial Phenol concentration of 20.0 mg L-1; pH 5.0; stirring rate

150 rpm; contact time of 60 min; temperature 25 �C)

100 150 200 250 300

92

94

96

98

% of adsorption q

e (mg/g)

Stirring rate (rpm)

% o

f ads

orpt

ion

0.92

0.93

0.94

0.95

0.96

0.97

0.98

qe (m

g/g)

Fig. 7 Effect of stirring rate on adsorption of phenol using OPA

(Initial Phenol concentration of 20.0 mg L-1; pH 5.0; adsorbent dose

2.0 g; contact time 60 min; temperature 25 �C)

10 20 30 40 50 60

90

92

94

96

98

% of adsorption q

e mg/g

Intial concentration (ppm)

% o

f ads

orpt

ion

0

2

qe (m

g/g)

Fig. 8 Effect of Initial concentration on adsorption of phenol using

OPA. (pH—5.0; adsorbent dose 2.0 g; stirring rate 150 rpm; contact

time 60 min; temperature 25 �C)

0 20 40 60 80 100 120 140 160 180 20030

40

50

60

70

80

90

100

% of removal q

e (mg/g)

Contact Time (minute)

% o

f rem

oval

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0q

e (mg/g)

Fig. 9 Effect of contact time on adsorption of phenol using OPA

(Initial concentration 20.0 mg L-1; pH 5.0; adsorbent dose 2.0 g;

stirring rate 150 rpm; temperature 25 �C)

276 Appl Water Sci (2015) 5:271–282

123

Adsorption isotherm

Equilibrium study provides information on the efficiency

of the adsorbent. An adsorption isotherm is usually char-

acterized by certain constant values, which express the

surface properties and affinity of the adsorbent. The most

widely used isotherm equations for modeling of the

adsorption of phenol are as follows:

1

qe¼ 1

KLqm

1

Ce

þ 1

qmð10Þ

log qe ¼ logKf þ1

nlogCe ð11Þ

qe ¼ qDR expð�Ke2DRÞ ð12Þ

qe ¼ B lnKT þ B lnCe ð13Þ

where qmax (mgg-1) and KL (L mg-1) are Langmuir

parameters related to maximum adsorption capacity and

free energy of adsorption, respectively. KF and n are the

Freundlich constants that indicate adsorption capacity and

adsorption intensity, respectively. RT/bT = B, where T is

the temperature (K), and R is the ideal gas constant

(8.315 Jmol-1K-1) and KT (L min-1) and bT are constants.

qm (mgg-1) is related to maximum adsorption capacity and

b is a constant related to the mean free energy of adsorption

per mole of the adsorbate (mol2J-2), and e is the polanyi

potential. The constant, b gives an idea about the mean free

energy Es (kJ mol-1) of adsorption per molecule of the

adsorbate when it is transferred to the surface of the solid

from infinity in the solution. Ce is the equilibrium

concentration in the aqueous solution and qe is the

equilibrium adsorption capacity of adsorbent. The

essential features of the isotherm can be expressed in

terms of a dimensionless constant separation factor (RL)

that can be defined by the following relationship

(Anirudhan and Radhakrishnan 2008):

RL ¼ 1

1þ KLC0

ð14Þ

According to both Table 3 and Fig. 12, the Langmuir

isotherm model shows excellent fitness to the experimental

data with high correlation coefficient at all temperatures.

The maximum phenol adsorption capacity onto OPA is

found to be 3.55 mgg-1 at 303 K.

The fitness of equilibrium data (Fig. 12) in Langmuir

isotherm advocates the monolayer coverage of phenol onto

OPA (Uddin et al. 2007). The essential features of Lang-

muir isotherm can be expressed in terms of a dimensionless

constant separation factor (RL). RL value indicates the

favorable adsorption of phenol onto OPA (Maheswari et al.

2009). Again from Table 4, it is clear that OPA is a good

adsorbent for phenol among the other reported adsorbents.

The Freundlich constant, KF indicates the adsorption

capacity of OPA and the value of KF is 1.011 mgg-1.

Furthermore, the value of ‘n’ at equilibrium is 1.90, indi-

cating favorable adsorption (Slejko 1985). From D–R iso-

therm the value of the adsorption energy is found to be

1.83 kJ mol-1 indicating the physisorption mechanism.

Again from Temkin constants (B) which is related to the

heat of adsorption is moderated. However, other equilib-

rium constant (KT) data (5.69 L/mg) suggest the maximum

binding energy between OPA and phenol molecules

(Table 5).

Adsorption kinetic models

Kinetic models are used to evaluate the rate of the

adsorption process and rate-controlling step. In the present

communication, the kinetic data obtained from batch

studies are analyzed using the following kinetic models

(Table 6).

Pseudo first� order : logðqe � qtÞ ¼ log qe � k1t

2:303

ð15Þ

Pseudo second� order :t

qt¼ 1

k2q2eþ 1

qet ð16Þ

The intra-particle diffusion:

qe ¼ kidt0:5 þ C ð17Þ

where qe and qt are concentrations of phenol at equilibrium

and at time t. k1 and k2 are constants of the pseudo-first-

and pseudo-second-order kinetic model. Kid is the constant

of intra-particle diffusion model and C is related to

boundary layer effect. The results of three kinetic models

are shown in the Table 5 to find out the best fit rate of

reaction for the adsorption of phenol onto OPA. According

to linear regression correlation coefficients (Table 5), the

rate of adsorption is found to follow the pseudo-second-

296 298 300 302 304 306 308 310 312 314

76

78

80

82

84

86

88

90

92

94

96

98 % of adsorption q

e (mg/g)

Temperature (K)

% o

f ads

orpt

ion

0.75

0.80

0.85

0.90

0.95

1.00

qe (m

/g)

Fig. 10 Effect of temperature (K) on adsorption of phenol using OPA

(Initial concentration 20.0mgL-1; pH—5.0; adsorbent dose 2.0 g;

Stirring rate 150 rpm; contact time 60 min.)

Appl Water Sci (2015) 5:271–282 277

123

order kinetic model and the theoretical qe value is closer to

the experimental qe value. In the view of the present

results, it can be said that the pseudo-second-order kinetic

model provides a good correlation for the adsorption of

phenol onto OPA compared to that of pseudo-first-order

model (Figs. 13, 14). Therefore, Pseudo-second-order

model is highly applicable for this adsorption process. To

identify the diffusion mechanism, the kinetic results are

analyzed using the intra-particle diffusion model (Weber

and Morris 1963).

3 4 5 6 7 865

70

75

80

85

90

95

100

% o

f rem

oval

pH

Networkout Exp.output

(a)

10 20 30 40 50 6086

88

90

92

94

96

98

% o

f rem

oval

Initial Conc.(mg/L)

Networkout Exp.output

(b)

(c) (d)

(e) (f)

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.540

50

60

70

80

90

100

% o

f rem

oval

Adsorbent dose(g)

Networkout Exp.output

100 150 200 250 300

92

93

94

95

96

97

98

% o

f rem

oval

Stiring rate(rpm)

Networout Exp.out

0 20 40 60 80 100 120 140 160 180 20030

40

50

60

70

80

90

100

% o

f rem

oval

Contact time(min)

Networkout Exp.out

300 305 310 315 320 325 330 335

767880828486889092949698

% o

f rem

oval

Temp.(K)

Networout Exp.out

Fig. 11 Agreement between

ANN outputs and experimental

data as a function of a pH,

b initial concentration

(mg L-1), c adsorbent dose,

d stirring rate, e contact time

(min) and f temperature (K)

Table 2 Equations of Isotherm models used in adsorption of phenol onto OPA

Isotherm models Linear expression Plot Parameters References

Langmuir 1qeq

¼ 1qmaxKLCe

þ 1qmax

1/qe vs. 1/Ce qmax = 1/intercept, KL = 1/(slope 9 qm) Langmuir (1916)

Frreundlich log qeq ¼ logKF þ 1nlogCe log (qe) vs. log (Ce) KF = intercept, 1/n = slope Freundlich (1906)

Temkin qe ¼ B lnKT þ B lnCe qe vs. ln Ce B = slope, KT = intercept Wasewar et al. (2009)

DR lnqe ¼ lnqm �be2Es ¼ 1ffiffiffiffi2b

p ln qe vs. e2 b = slope, qm = intercept Kalavathy et al. (2010)

278 Appl Water Sci (2015) 5:271–282

123

Thermodynamic parameters

The Gibbs free energy (DG�) for the adsorption of phenol

onto OPA is calculated following the equation

(DG� = -RT ln K) and data are presented in Table 7. The

values for DH� and DS� are determined from the slope and

intercept of the plot of DG� vs. T (figure not shown) and are

also listed in Table 7. The negative value of DG� at all

temperature indicates the feasibility as well as spontaneity

of the phenol adsorption on OPA. Decrease in the value of

DG� with increase in temperature suggests that lower

temperature favors the adsorption (Saha et al. 2010). The

negative value of DH� implies that the adsorption phe-

nomenon is exothermic while the negative value of DS�suggests the process is enthalpy oriented (Saha et al. 2010).

Activation energy

The activation energy (Ea) usually provides important

information on the mechanism of adsorption reaction.

Using the pseudo-second-order rate constant, k2 the acti-

vation energy, Ea for the adsorption of phenol on OPA is

determined using the Arrhenius equation (18).

ln k ¼ lnA� Ea

RTð18Þ

where k is the rate constant, A is the Arrhenius constant, Ea

is the activation energy (kJ mol-1), R is the gas constant

(8.314 J mol-1K-1) and T is the temperature (K). By

plotting lnk2 vs. 1/T, Ea is obtained from the slope of the

linear plot (Fig. 15). The activation energy is very signif-

icant to evaluate whether the entire adsorption reaction is

physisorption or chemisorptions. If the value for activation

energy lies between 8 and 16 kJ mol-1, it is chemisorp-

tions, and when it is below 8 kJ mol -1, it is physisorption

(Hai-jun et al. 2009). Here the Ea value is

-18.001 kJ mol-1. The measured Ea value suggests that

the adsorption may be a physical adsorption.

Table 3 Adsorption isotherm constants for adsorption of phenol onto

OPA

Parameters of adsorption isotherm models Values

Langmuir

qm (mgg-1) 3.55

KL 0.45

R2 0.98

RL 0.18

Freundlich

KF 1.01

1/n 0.52

R2 0.91

D–R

qm (mgg-1) 7.22

B 0.15

Es 1.83

R2 0.95

Tempkin

KT 5.69

B 0.72

R2 0.93

0 1 2 3 4 5 6 7

0.5

1.0

1.5

2.0

2.5

3.0

q e(m

g/g)

Ce(mg/L)

Experimental Langmuir Freundlich Temkin D-R

Fig. 12 Comparison between the measured and modeled isotherm

profiles for the adsorption of phenol by orange peel ash (experimental

conditions: Initial concentration 20.0 mg L-1; pH—5.0; adsorbent

dose 2.0 g; stirring rate 150 rpm; contact time 60 min, temperature

298 K)

Table 4 Maximum adsorption capacities, qmax (mg/g), for the

adsorption of phenolic compounds by various adsorbents

Adsorbents qmax (mg/g) References

S. muticum 4.6 Rubin et al. (2006)

Lignite 10.0 Polat et al. (2006)

Rice husk 4.5 Ahmaruzzaman and Sharma (2005)

Chicken feathers 19.5 Banat and Al-Asheh (2000)

Bentonite 1.712 Banat et al. (2000)

Orange peel ash 3.55 Present study

Table 5 Kinetic model constants for adsorption of phenol onto OPA

Kinetic models Experimental qe

Pseudo-first-order qe = 0.368 0.973

K1 = 0.006

R12 = 0.316

Pseudo-second order qe = 1.133

K2 = 0.0374

R22 = 0.9755

Intra-particle diffusion Kid = 0.002

C = 0.056

R2 = 0.91

Appl Water Sci (2015) 5:271–282 279

123

Tested with ANN model

The trained ANN model is tested and validated with the

experimental results to estimate the phenol concentration.

The network is trained with given input data set (Table 8).

The training phase is completed after 6,00,000 epochs

(Fig. 16). The lower value of MSE indicates the degrees of

error that means network gives correct output. It is also

clear from the Fig. 16 that with the increasing epoch the

network is trained more accurately and subsequently more

accurate and perfect output is achieved.

After the training phase the network shows optimum

result describing the dynamics of phenol adsorption from

aqueous solution. Finally in testing phase, the results show

that the network output is matched with the experimental

output (Fig. 11a–f). It is observed from Fig. 16 that MSE

value is 0.0017 for one epoch that decreases with the

increasing number of epochs and found minimum (0.0006)

at 11 numbers of epochs. So the 11 number of hidden layer

may be considered as optimum for this ANN model.

Conclusion

It has been found that the OPA has enough potentiality to

remove phenol from water. The operational parameters

such as pH, initial phenol concentration, adsorbent dose,

contact time, stirring rate and temperature have significant

influence on the adsorption efficiency of OPA. The

Table 6 Equations of kinetic models used in adsorption of phenol onto OPA

Parameters Linear form References Plot Parameters

Kinetic models

Pseudo-first-order logðqe � qtÞ ¼ log qe � k1t2:303

Theivarasu et al. 2011 log (qe-qt) vs. t qe = intercept,

k1 = (slope 9 2.303)

Pseudo-second-order tqt¼ 1

k2q2eþ t

qeHo and Mc Kay (1998) t/qt vs. t slope = 1/qe,

intercept = 1k2q2e

Intra-particle diffusion

model

q = kid t� ? C Weber and Morris (1962) q vs. t1/2 kid = slope

C = intercept

Pseudo-first-order kinetic model

y = 0.0027x - 0.4335

R2 = 0.316

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0 50 100 150 200

t

log

(qe-

qt)

Fig. 13 Pseudo-first-order adsorption kinetic model for phenol

adsorption with OPA

Pseudo-second order kinetic model

y = 0.8823x + 20.808

R2 = 0.9755

0

2040

6080

100

120140

160180

200

0 50 100 150 200

t

t/qt

Fig. 14 Pseudo-second-order adsorption kinetic model for phenol

adsorption with OPA

Table 7 Thermodynamic parameters for phenol adsorption onto

OPA

DG� (kJ mol-1) DH� (kJ mol-1) DS� (kJ mol-1)

298 303 308 313 -85.09 -0.25

-9.1 -7.825 -6.55 -5.275

lnk2 vs 1/T

y = 2174.2x - 10.568

R2 = 0.8757

-4

-3.9

-3.8

-3.7

-3.6

-3.5

-3.4

-3.3

-3.2

0.00305 0.0031 0.00315 0.0032 0.00325 0.0033 0.00335 0.0034

1/T

lnk2

Fig. 15 Plot of lnk2 vs. 1/T

280 Appl Water Sci (2015) 5:271–282

123

maximum adsorption capacity is 3.55 mgg-1 at 20 mg L-1

initial phenol concentration. The multi-layer ANN model-

ing technique can be applied to optimize the process. The

Back Propagation Algorithm (BPA) is found to be the best

algorithm with a minimum mean squared error (MSE) for

training 0.00528. The temperature has strong influence on

the adsorption process and the maximum removal is

observed at 298 K. The kinetic study reveals that the

adsorption process usually follows pseudo-second order.

Langmuir isotherm model is in accordance with the

experimental data. Moreover, thermodynamic parameters

are in favor of exothermic and spontaneous nature of

adsorption of phenol onto OPA.

Acknowledgments The authors are grateful to Dr. Alak Kumar

Ghosh, Associate Professor, Department of Chemistry, Burdwan

University, Burdwan, West Bengal, India for recording FTIR data.

The authors would like to extend their gratitude to Dr Srikanta

Chakraborty, In charge of SEM, USIC, University of Burdwan, West

Bengal, India for SEM study.

Open Access This article is distributed under the terms of the

Creative Commons Attribution License which permits any use, dis-

tribution, and reproduction in any medium, provided the original

author(s) and the source are credited.

References

Ademiluy FT, Amsfi SA, Amakama NJ (2009) Adsorption and

Treatment of organic contaminants using activated carbon from

waste Nigerian Bamboo. J Appl Sci Environ Manage 13:39–47

Ahmaruzzaman M, Sharma DK (2005) Adsorption of phenol from

waste. Water J Colloid Interface Sci 287(1):14–24

Aksu Z, Yener J (2001) A comparative adsorption/biosorption study

of monochlorinated Phenols onto various sorbent. Waste Manag

21:695–702

Anirudhan TS, Radhakrishnan PG (2008) Thermodynamics and

kinetics adsorption of Cu (II) from aqueous solution on to a new

cation exchanger derived from tamarind fruit shell. J Chem

Thermodyn 40:702–709

APHA, AWWA, WEF (1995) Standard methods for the examination

of water and Wastewater, 19th edn. Washington

Asma S, Zainal A (2009) Adsorption of phenol by activated carbon

produced from decanter cake, Eng D thesis, University Malaysia

Pahan

Banat FA, Al-Asheh S (2000) Biosorption of phenol by chicken

feather. Environ Eng Policy 2(2):85–90

Banat FA, Al-Bashir B, Al-Asheh S, Hayajneh O (2000) Adsorption

of phenol by bentonite. Environ Pollut 107:391–398

Bhattacharjya RK, Datta B, Satish MG (2007) Artificial neural

networks approximation of density dependent saltwater intrusion

process in coastal aquifers. J Hydrol Eng 12(3):273–282

Bhatti HN, Nasir AW, Hanif MA (2010) Efficacy of Daucus Carotal

Waste biomass for the removal of chromium from the aqueous

solution. Desalination 253:78–87

Carsky M, Do DD (1999) Neural network modeling of adsorption of

binary vapour mixtures. Adsorption 5:183–192

Caturla F, Martin-Martinez JM, Molina-Sabio M, Rodrignez-Reinoso

F, Torregrosa R (1998) Adsorption of substituted phenols on

activated carbon. J Colloid Interface Sci 124:528–534

Table 8 The outputs of MLP

Learning rate 0.001

No. of iterations 600,000

Testing phase Data pH Concentration Dose Time Stirring rate Temperature Network output Exp. output

Sigmoid function 5.6 20 0.5 40 150 303 60.26 64.05

5.6 20 3 40 150 303 70.33 69.56

5.6 60 2 40 150 303 87.67 89.85

Tanh function 5.6 20 0.5 40 150 303 62.18 64.05

5.6 20 3 40 150 303 77.77 69.56

5.6 60 2 40 150 303 88.44 89.85

TAN1.5H function 5.6 20 0.5 40 150 303 62.07 64.05

5.6 20 3 40 150 303 77.38 69.56

5.6 60 2 40 150 303 90.002 89.85

Fig. 16 Relation between the MSE and number of epochs

Appl Water Sci (2015) 5:271–282 281

123

Chelani AB, Chalapati Rao CV, Phadke KM, Hasan MZ (2004)

Prediction of sulphur dioxide concentration using artificial neural

networks. Environ Model Softw 17:161–168

Cuco APC, Neto AJS, Velho HFC, Sousa FL (2009) Solution of an

inverse adsorption problem with an epidemic genetic algorithm

and the generalized extremal optimization algorithm. Inverse

Prob Sci Eng 17:289–302

Eahart JP, Won K, Wang HY, Prausnitz JM (1977) Recovery of

organic pollutants via solvent extraction. Chem Eng Prog

73:67–73

Freundlich H (1906) Over the adsorption in solution. Z Phys Chem

57:385–470

Gholami F, Mahvi AH, Omrani GhA, Nazmara Sh, Ghasri A (2006)

Removal of Chromium from aqueous solution. Iran J Environ

Health Sci and Eng 3:97–102

Hai-jun L, Mao-tian L, Jin-li Z (2009) A kinetic study on the

Adsorption of Chromium (VI) onto a natural Material used as

Landfill Liner. E J Geotech Eng 14:1–10

Hamza HA, Atieh MA, Laoui T (2012) Removal of phenol by carbon

nanotubes and activated carbon—a comparative analysis, sixteen

international water technology conference, IWTC, 16, Istanbul,

Turkey

Hegazy AK, Abdel-Ghani NT, El-Vhaghaby GA (2013) Adsorption

of phenol onto activated carbon from Rhazya stricta: determi-

nation of the optimal experimental parameters using factorial

design. Appl Water Sci. doi:10.1007/s13201-013-0143-9

Ho YS, McKay G (1998) Kinetic model for lead (II) sorption onto

peat. Ads Sci Technol 16:943–955

Imagawa A, Seto R, Nagaosa Y (2000) Adsorption of chlorinated

hydrocarbons from air and aqueous solutions by carbonized rice

husk. Carbon 38:623–641

Kalavathy H, Karthik B, Miranda LR (2010) Removal and recovery

of Ni and Zn from aqueous solution using activated carbon from

Hevea brasiliensis: batch and column studies. Colloid Surf B:

Biointerface 78:291–302

Langmuir I (1916) The constitution and fundamental properties of

solids and Liquids. J Am Chem Soc 38:2221–2295

Li XM, Tang YR, Xuan ZX, Liu YH, Luo F (2007) Study on the

preparation of orange peel cellulose adsorbents and biosorption

of Cd2? from aqueous solution. Sep Purif Technol 55(1):69e75

Lotfy HR, Misihairabgwi J, Mutwa MM (2012) The preparation of

activated carbon from aqroforestry waste for waste water

treatment. Afr J Pure Appl Chem 6:149–156

Maheswari P, Venilamani N, Madhavakrishnan S, Sayed Sahabudeen

PS, Nadavala SK, Veera MB (2009) Biosorption of phenolic

compounds by Trametes versicolor polyporus Fungus. Adsorpt

Sci Technol 27:231–237

Mehrizad A, Aghaie M, Gharbani P, Dastmatch S, Monajjemi M,

Zare K (2009) Comparison of 4-Chloro-2-nitrophenol adsorption

on single walled and multi-walled carbon nanotubes. Iran J

Environ Health Sci Eng 9:5. doi:10.1186/1735-2746-9-5

Ning-chuan F, Xue-yi G (2012) Characterization of adsorptive

capacity and mechanisms on adsorption of copper, lead and

zinc by modified orange peel. Trans Nonferrous Met Soc China

22:1224–1231

Ogwueleka TCh, Ogwueleka FN (2010) Modelling energy content of

municipal solid waste using artificial neural network. Iran J

Environ Health Sci Eng 7:259–266

Olafadehan OA, Jinadu OW, Safami L, Popoola OT (2012) Treatment

of Brewery waste water effluent using activated carbon prepared

from coconut shell. Inter J Appl Sc Technol 2:165–178

Polat H, Molva M, Polat M (2006) Capacity and mechanism of

phenol adsorption on lignite. Int J Miner Process 79:264–273

Rengaraj S, Seuny-Hyeon M, Sivabalan R (2002) Agricultural solid

waste for the removal of organics: adsorption of phenol from

water and wastewater by Palm seed coat activated carbon. Waste

Manage 22:543–548

Rubin E, Rodriguez P, Herrero R, de Sastre Vicente ME (2006)

Biosorption of phenolic compounds by the brown alga sargas-

sum muticum. J Chem Technol Biotechnol 81:1093–1099

Saha P, Chowdhury S, Gupta S, Kumar I (2010) Insight into

adsorption equilibrium, kinetics and thermodynamics of Mala-

chite Green onto clayey soil of Indian Origin. Chem Eng J

165:874–882

Shan W, Fang D, Zhao Z, Shuang Y, Ning L, Xing Z, Xiong Y (2012)

Application of orange peel for adsorption separation of molyb-

denum(VI) from Re-containing industrial effluent. Biomass

Bioenergy 37:289–297

Slejko F (1985) Adsorption technology a step by step approach to

process evaluation and application. Marcel Decker, New York,

p 13

Theivarasu C, Mylsamy S, Sivakumar N (2011) Cocoa shell as

adsorbent for the removal of methylene blue from aqueous

solution: kinetic and equilibrium study. Univers J Environ Res

Technol 1:70–78

Uddin MT, Islam MS, Abedin MZ (2007) Adsorption of phenol from

aqueous solution by water hyacinth ash. ARPN J Eng Appl Sci

2:11–17

Wasewar KL, Kumar S, Prasad B (2009) Adsorption of tin using

granular activated carbon. J Environ Prot Sci 3:41–52

Weber WJ, Morris JC (1962) Advances in water pollution research:

Removal of biologically resitant pollutants from waste water by

adsorption. In: Proceeding of international conference on water

pollution symposium, vol 2, pp 231–266

Weber WJ, Morris JC (1963) Kinetics of adsorption on carbon from

solution. J Sanit Eng Div Am Soc Civil Eng 89:31–39

WHO (2008) Guidelines for Drinking Water Quality: Incorporating

First and Second Addenda. 3rd edn. vol 1. World Health

Organization, Geneva

Zeng X, Fan Y, Wu G, Wang C, Shi R (2009) Enhanced adsorption of

phenol from water by a novel polar post-crosslinked polymeric

adsorbent. J Hazard Mater 169:1022–1028

282 Appl Water Sci (2015) 5:271–282

123